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    Design Optimization Engineer for Responsive Materials - Livermore, United States - Lawrence Livermore National Laboratory

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    Description
    Company Description

    Join us and make YOUR mark on the World

    Are you interested in joining some of the brightest talent in the world to strengthen the United States' security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.

    We are committed to a diverse and equitable workforce with an inclusive culture that values and celebrates the diversity of our people, talents, ideas, experiences, and perspectives. This is important for continued success of the Laboratory's mission.

    Pay Range

    $112,800 - $129,240 Annually

    Please note that the pay range information is a general guideline only. Many factors are taken into consideration when setting starting pay including education, experience, the external labor market, and internal equity.

    Job Description

    We have an opening for a Design Optimization Engineer - Postdoctoral Researcher to conduct basic and applied research in the optimization, simulation, and design of physical and engineered systems. This role involves working both collaboratively and independently to extend and develop physics-based design optimization tools. Our team is working to advance the state-of-the-art in support of Laboratory missions. This position is in the Computational Engineering Division (CED), within the Engineering Directorate.

    In this role, you will

    • Develop and implement high-performance computing (HPC) algorithms for the simulation and design optimization of systems governed by nonlinear, transient, multiscale and multiple physical phenomena, including uncertainties in geometry and material properties.
    • Contribute to identifying and solving challenging design optimization problems with gradient-based optimization approaches.
    • Develop continuum models and implement them in our HPC numerical analysis software to solve the governing partial differential equations of the physical system, to predict design responses of interest.
    • Demonstrate our design optimization capabilities and establish credibility with mission customers.
    • Document research findings through presentations and peer-reviewed journal articles, and contribute to identifying future research directions and proposals that will provide opportunities in the field.
    • Collaborate with multidisciplinary teams to integrate design optimization tools with other simulation and analysis frameworks.
    • Stay current with technological advancements by updating and enhancing your knowledge of the latest HPC algorithms, simulation techniques, and design optimization methods.
    • Mentor and train junior researchers, interns, and other team members in the use of design optimization tools and methodologies.
    • Perform other duties as assigned.
    Qualifications
    • PhD in Engineering, Mathematics, Computational Science, or a related field.
    • Experience developing custom simulation tools using continuum discretization approaches (e.g., finite elements) for physical systems.
    • Proficiency in programming languages such as C/C++/FORTRAN and scripting languages including Python.
    • Knowledge of HPC and one or more associated parallel programming interfaces, such as MPI, OpenMP, or CUDA.
    • Ability to perform independent research.
    • Proficient verbal and written communication skills needed to effectively collaborate in a team environment, to present and explain technical information, to document work, and to prepare and present successful proposals and high-quality research papers.

    Qualifications We Desire

    • Expertise in design sensitivity analysis via direct (forward) and adjoint (backward) methods, shape/topology optimization and other design optimization techniques; familiarity with machine learning and data science techniques.
    • Experience simulating and designing systems that involve complex nonlinear, transient, multiphysics models; proficient in nonlinear solvers, and linear algebra concepts for solving challenging linear systems.
    • Experience working in a collaborative software development environment, including experience with version control systems, such as Git.
    • Adept at tackling complex technical challenges with a logical and analytical approach, working effectively with experts from various fields, and promoting an inclusive and diverse work environment.
    Additional Information

    All your information will be kept confidential according to EEO guidelines.

    Position Information

    This is a Postdoctoral appointment with the possibility of extension to a maximum of three years, open to those who have been awarded a PhD at time of hire date.

    Why Lawrence Livermore National Laboratory?

    • Included in 2024 Best Places to Work by Glassdoor
    • Flexible Benefits Package
    • 401(k)
    • Relocation Assistance
    • Education Reimbursement Program
    • Flexible schedules (*depending on project needs)
    • Inclusion, Diversity, Equity and Accountability (IDEA) - visit
    • Our core beliefs - visit
    • Employee engagement - visit

    Security Clearance

    This position requires either no security clearance, or a Department of Energy (DOE) L-level or Q-level clearance depending on the particular assignment.

    If you are selected and a security clearance is required, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing. L and Q-level clearances require U.S. citizenship.

    If no security clearance is required, but your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process. This process includes completing an online background investigation form and receiving approval of the background check. (This process does not apply to foreign nationals.)

    Pre-Employment Drug Test

    External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.

    How to identify fake job advertisements

    Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under "Find Your Job" of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond.

    To learn more about recruitment scams:

    Equal Employment Opportunity

    We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.

    We invite you to review the Equal Employment Opportunity posters which include EEO is the Law and Pay Transparency Nondiscrimination Provision.

    Reasonable Accommodation

    Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory. If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request.

    California Privacy Notice

    The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here.



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